Method and apparatus for building image model

ABSTRACT

A method and apparatus for building an image model, where the apparatus generates a target image model that includes layers duplicated from a layers of a reference image model and an additional layer, and trains the additional layer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2018-0076226, filed on Jul. 2, 2018, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to training an image model.

2. Description of Related Art

To address an issue of classifying an input pattern as a group, researchis being conducted on trying to apply an efficient pattern recognitionmethod to an actual computer. The research includes research on anartificial neural network (ANN) obtained by modeling pattern recognitioncharacteristics using mathematical expressions. To address the aboveissue, the ANN employs an algorithm that mimics learning an ability tolearn. The ANN generates mapping between input patterns and outputpatterns using the algorithm, which indicates that the ANN has alearning capability. Also, the ANN has a generalization capability togenerate a relatively correct output with respect to an input patternthat was not used for training based on a result of the training.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, there is provided a method of building an imagemodel, the method including generating a target image model including anadditional layer and remaining layers that are same as layers of areference image model and, and training the additional layer of thetarget image model based on the reference image model.

The generating of the target image model may include generating thetarget image model by connecting the additional layer to a layer locatedon an input side of the remaining layers in the target image model.

The generating of the target image model may include initializing theadditional layer by assigning a random value to each nodes of theadditional layer.

The training of the additional layer may include determining a referencemodel output from an image with a converted training input, based on thereference image model, and training the target image model based on thereference model output.

The determining of the reference model output may include generating aconversion image by converting the training input based on an inputlayer of the reference image model, and computing the reference modeloutput from the conversion image.

The training of the additional layer may include training the targetimage model based on a reference model output and a target model outputthat are based on the reference image model and the target image model,respectively.

The training of the additional layer may include computing an errorbased on the reference model output and the target model output, andupdating a parameter of at least a portion of the remaining layers andthe additional layer in the target image model based on the computederror.

The updating of the parameter may include repeating updating of theparameter until an error between the reference model output and thetarget model output converges.

The training of the additional layer may include updating a parameter ofthe additional layer.

The training of the additional layer may include updating a parameter ofthe additional layer while maintaining parameters of the remaininglayers in the target image model.

The generating of the target image model may include generating theremaining layers of the target image model by duplicating parameters anda layer structure of the reference image model, and connecting theadditional layer to the remaining layers.

The method may include updating parameters of the remaining layers andthe additional layer in the target image model, in response to acompletion of training of the additional layer.

A number of nodes in the additional layer may be greater than or equalto a number of nodes in one of the layers of the reference image modeland is less than or equal to a number of nodes in an input layer of thetarget image model.

The method may include acquiring an input image, determining a label ofthe input image, and additionally training the target image model basedon the input image and the label.

The determining of the label may include determining the label based ona user input associated with the acquiring of the input image.

The method may include generating an additional image model comprisingan additional layer connected to the target image model, in response toa completion of training of the target image model, and training theadditional layer of the additional image model based on an output of thetarget image model.

The target image model may be configured to receive an image with aresolution higher than an input resolution of the reference image model.

The training of the additional layer may include training the additionallayer based on feature data output from at least a portion of the layersof the reference image model and feature data output from at least aportion of the remaining layers of the target image model.

The resolution of the image may be greater than a resolution of theconversion image.

A number of nodes in the additional layer may be greater than a numberof nodes in any of the layers of the reference image model.

In another general aspect, there is provided an apparatus for buildingan image model, the apparatus including a processor configured togenerate a target image model that includes an additional layer andremaining layers that are same as layers of a reference image model, andto train the additional layer of the target image model based on thereference image model, and a memory configured to store the trainedtarget image model.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a configuration of an image model.

FIG. 2 illustrates an example of building a target image model based ona reference image model.

FIGS. 3 and 4 are diagrams illustrating examples of an image modelbuilding method.

FIG. 5 illustrates an example of generating and initializing a targetimage model.

FIG. 6 is a diagram illustrating an example of training a target imagemodel.

FIG. 7 illustrates an example of computing an error during training.

FIG. 8 illustrates an example of training all portions of a target imagemodel.

FIG. 9 illustrates an example of growing a target image model.

FIGS. 10 and 11 are diagrams illustrating examples of image modelbuilding apparatus.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

Various modifications may be made to the following examples. Here, theexamples are not construed as limited to the disclosure and should beunderstood to include all changes, equivalents, and replacements withinthe idea and the technical scope of the disclosure.

The terminology used herein is for the purpose of describing particularexamples only and is not to be limiting of the examples. As used herein,the singular forms are intended to include the plural forms as well,unless the context clearly indicates otherwise. For example, thearticles “a,” “an,” and “the” are intended to include the plural formsas well, unless the context clearly indicates otherwise. It should befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, components or a combinationthereof, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

Although terms such as “first,” “second,” and “third” may be used hereinto describe various members, components, regions, layers, or sections,these members, components, regions, layers, or sections are not to belimited by these terms. Rather, these terms are only used to distinguishone member, component, region, layer, or section from another member,component, region, layer, or section. Thus, a first member, component,region, layer, or section referred to in examples described herein mayalso be referred to as a second member, component, region, layer, orsection without departing from the teachings of the examples.

The use of the term ‘may’ herein with respect to an example orembodiment, e.g., as to what an example or embodiment may include orimplement, means that at least one example or embodiment exists wheresuch a feature is included or implemented while all examples andembodiments are not limited thereto.

Hereinafter, examples will be described in detail with reference to theaccompanying drawings, and like reference numerals in the drawings referto like elements throughout.

FIG. 1 illustrates an example of a configuration of an image model.

A neural network 100 corresponds to an example of a deep neural network(DNN) or an n-layer neural network. The DNN includes, for example, afully connected network, a convolutional neural network (CNN), a deepconvolutional network, or a recurrent neural network (RNN), a deepbelief network, a bi-directional neural network, a restricted Boltzmanmachine, or may include different or overlapping neural network portionsrespectively with full, convolutional, recurrent, and/or bi-directionalconnections. The neural network 100 maps, based on deep learning, inputdata and output data that are in a non-linear relationship, to perform,for example, an object classification, an object recognition, a speechrecognition or an image recognition. In an example, deep learning is amachine learning scheme to solve a problem such as a recognition ofspeech or images from a big data set. Through supervised or unsupervisedlearning in the deep learning, input data and output data are mapped toeach other.

In the following description, a recognition includes a verification andan identification. The verification is an operation of determiningwhether input data is true or false, and the identification is anoperation of determining which one of a plurality of labels is indicatedby input data.

In an example, the neural network 100 may include an input sourcesentence (e.g., voice entry) instead of an input image. In such anexample, a convolution operation is performed on the input sourcesentence with a kernel, and as a result, the feature maps are output.The convolution operation is performed again on the output feature mapsas input feature maps, with a kernel, and new feature maps are output.When the convolution operation is repeatedly performed as such, arecognition result with respect to features of the input source sentencemay be finally output through the neural network 100.

Referring to FIG. 1, the neural network 100 includes an input layer 110,a hidden layer 120, and an output layer 130. Each of the input layer110, the hidden layer 120, and the output layer 130 includes a pluralityof artificial neurons. An artificial neuron may be referred to as a“node”.

For convenience of description, the hidden layer 120 includes threelayers as shown in FIG. 1, however, example are not limited thereto, andthe hidden layer 120 may include various number of layers withoutdeparting from the spirit and scope of the illustrative examplesdescribed. Although the neural network 100 includes a separate inputlayer to receive input data as shown in FIG. 1, in an example, the inputdata is directly input to the hidden layer 120. In the neural network100, artificial neurons of layers other than the output layer 130 areconnected to artificial neurons of a next layer via links to transmitoutput signals. A number of links corresponds to a number of artificialneurons included in the next layer. The links are referred to as“synapses”.

To each of artificial neurons included in the hidden layer 120, anoutput of an activation function associated with weighted inputs ofartificial neurons included in a previous layer is input. The weightedinputs are obtained by multiplying a synaptic weight to inputs of theartificial neurons included in the previous layer. The synaptic weightis referred to as a parameter of the neural network 100. The activationfunction includes, for example, a sigmoid function, a hyperbolic tangent(tan h) function, or a rectified linear unit (ReLU) function. Anonlinearity is formed in the neural network 100 by the activationfunction. To each of artificial neurons included in the output layer130, weighted inputs of artificial neurons included in a previous layerare input.

When input data is provided, the neural network 100 calculates afunction value based on a number of classes that are to be classifiedand recognized in the output layer 130 via the hidden layer 120, andclassifies and recognizes the input data as a class having a greatestvalue among the classes. The neural network 100 may classify orrecognize input data, and a classification and recognition process ofthe neural network 100 will be described as a recognition process belowfor convenience of description. The following description of therecognition process is equally applicable to a classification processwithout departing from the spirit of the present disclosure.

When a width and a depth of the neural network 100 are sufficientlylarge, the neural network 100 has a capacity large enough to implementan arbitrary function. When the neural network 100 learns a sufficientlylarge quantity of training data through an appropriate learning process,an optimal recognition performance is achieved.

In an example, the neural network 100 is embodied as an architecturehaving a plurality of layers including an input image, feature maps, andan output. In the neural network 100, a convolution operation isperformed on the input image with a filter referred to as a kernel, andas a result, the feature maps are output. The convolution operation isperformed again on the output feature maps as input feature maps, with akernel, and new feature maps are output. When the convolution operationis repeatedly performed as such, a recognition result with respect tofeatures of the input image may be finally output through the neuralnetwork 100.

In the present disclosure, an image model has a machine learningstructure that is trained to output a recognition result when an imageis input. For example, the image model includes the above-describedneural network 100, however, examples are not limited thereto. An imagemodel building apparatus trains a grown image model. For example, animage model building technology is applied to a recognition of an objectrelated to autonomous driving. The image model building apparatus trainsan image model based on an image output from a camera installed in avehicle, to effectively generate an image model that supports ahigh-resolution image.

The image model building apparatus is an apparatus for building an imagemodel. For example, the image model building apparatus generates andtrains a target image model based on a reference image model. Anoperation of building an image model includes an operation of generatingand training an image model. An image recognition apparatus recognizesan input image based on an image model that is built. For example, theimage recognition apparatus identifies an object appearing in the inputimage based on a built target image model. However, examples are notlimited thereto, and the image model building apparatus is integratedwith the image recognition apparatus.

FIG. 2 illustrates an example of training a target image model based ona reference image model.

An image model includes a plurality of layers. A connection relationshipbetween the plurality of layers varies depending on a design. In anexample, a reference image model 210 is an original image model that iscompletely trained, and is designed to receive, as an input, an imagewith a first resolution. A target image model 220 is a new image modelthat is to be trained, and is designed to receive, as an input, an imagewith a second resolution. In an example, the second resolution is higherthan the first resolution. An image model building apparatus generatesand trains the target image model 220 based on the reference image model210.

The reference image model 210 and the target image model 220 includeinput layers 211 and 221, convolutional layers 212 and 222, customizedparts 213 and 223, and classifiers 214 and 224, respectively.

The input layers 211 and 221 are layers configured to receive inputimages. The input layer 221 of the target image model 220 receives animage with the second resolution, and the input layer 211 of thereference image model 210 receives an image with the first resolution.Thus, the target image model 220 is used to extract, recognize, andclassify features of an image with a resolution higher than that of animage of the reference image model 210.

The convolutional layers 212 and 222 extract feature data from the inputimages. The feature data is data obtained by abstracting an input image,and is expressed as, for example, a feature vector. When a number ofconvolutional layers 212 and 222 increases, an abstraction level alsoincreases. For example, when a number of convolutional layers includedin an image model increases, an image recognition apparatus represents afeature of an input image with a higher resolution using the imagemodel. However, when the number of convolutional layers 212 and 222increases, a number of parameters that need to be trained also increase.When the number of parameters that need to be trained increases, avanishing level of gradient information increases during training of animage model, thereby increasing the difficulty level of training theimage model. The image model building apparatus effectively trains theimage model while extending the convolutional layers 212 and 222.

The customized parts 213 and 223 are designed to solve a given problem.In an example, the customized parts 213 and 223 are region proposalnetworks (RPNs) in a faster region-based convolutional neural network(FR-CNN). In another example, the customized parts 213 and 223 areadversarial spatial dropout networks (ASDNs) in a generative adversarialnetwork (GAN). In another example, the customized parts 213 and 223 areomitted based on a given problem.

The classifiers 214 and 224 classify classes of input data. For example,the classifiers 214 and 224 are networks to classify categories ofobjects appearing in an input image for object recognition. Theclassifiers 214 and 224 output information indicating an identification(ID) of an object. The classifiers 214 and 224 include fully-connectedlayers. However, when a final purpose of the image model is to extract afeature of an input image, the classifiers 214 and 224 are removed fromthe image model.

Although FIG. 2 shows four layers, that is, layers L1, L2, L3 and L4 asthe convolutional layers 212 of the reference image model 210, a numberof convolutional layers 212 is not limited thereto. A convolutionallayer close to an input layer extracts a more detailed feature of theinput image. The reference image model 210 is, for example, an imagemodel that is completely trained based on training data that includes animage with a first resolution.

In FIG. 2, the target image model 220 has a structure that is formed byexpanding the reference image model 210. For example, the convolutionallayers 222 of the target image model 220 further include one additionallayer, for example, a layer L0, in comparison to the convolutionallayers 212 of the reference image model 210, however, a number ofadditional layers is not limited thereto. An example of efficientlytraining the target image model 220 that includes a larger number ofparameters and a larger number of layers than those of the referenceimage model 210 will be described below.

The image model building apparatus trains the target image model 220 fora high-resolution image, based on the reference image model 210 that istrained based on a low-resolution image. For example, a camera sensor ofan image recognition apparatus (for example, an autonomous vehicle) ischanged to support a higher resolution. In this example, when thereference image model 210 is trained based on a low-resolution image, aperformance (for example, a recognition rate) for a high-resolutionimage decreases.

The image model building apparatus acquires new training data. Forexample, the new training data includes a high-resolution image. Theimage model building apparatus generates a conversion image byconverting the high-resolution image (for example, an image with asecond resolution) of the new training data into an image with a lowresolution (for example, a first resolution) in operation 201. The imagemodel building apparatus computes an output based on the reference imagemodel 210 from the conversion image. The image model building apparatusgenerates an output based on the target image model 220 from an inputimage of the new training data. In operation 209, the image modelbuilding apparatus computes an error from the output based on thereference image model 210 and the output based on the target image model220. The image model building apparatus trains the target image model220 based on the computed error. Examples of generating and training thetarget image model 220 will be further described below.

FIGS. 3 and 4 are diagrams illustrating examples of an image modelbuilding method.

FIG. 3 illustrates an example of an image model building method. Theoperations in FIG. 3 may be performed in the sequence and manner asshown, although the order of some operations may be changed or some ofthe operations omitted without departing from the spirit and scope ofthe illustrative examples described. Many of the operations shown inFIG. 3 may be performed in parallel or concurrently. One or more blocksof FIG. 3, and combinations of the blocks, can be implemented by specialpurpose hardware-based computer that perform the specified functions, orcombinations of special purpose hardware and computer instructions. Inaddition to the description of FIG. 3 below, the descriptions of FIGS.1-2 are also applicable to FIG. 3, and are incorporated herein byreference. Thus, the above description may not be repeated here.

Referring to FIG. 3, in operation 310, an image model building apparatusgenerates a target image model. In an example, the target image modelincludes the same layers as a plurality of layers of a reference imagemodel, and an additional layer. In the present disclosure, the samelayers as the layers of the reference image model are layers other thanthe additional layer in target image model, and are referred to as“remaining layers” in the present disclosure. The image model buildingapparatus generates remaining layers of the target image model byduplicating parameters and a layer structure of the reference imagemodel. In an example, the image model building apparatus connects theadditional layer to the remaining layers. For example, the image modelbuilding apparatus connects the additional layer to a layer (forexample, the layer L1 of FIG. 2) located on an input side among aplurality of layers duplicated from the reference image model, togenerate the target image model. However, a connection of the additionallayer is not limited thereto.

For example, the target image model includes layers having the sameparameters and the same structures as those of layers (for example,convolutional layers) included in the reference image model. A pluralityof layers of the target image model duplicated from the reference imagemodel are, for example, the layers L1, L2, L3, and L4 of FIG. 2. Thetarget image model further includes an additional layer connected to theplurality of layers duplicated from the reference image model. Theadditional layer is, for example, the layer L0 of FIG. 2.

In operation 320, the image model building apparatus trains theadditional layer of the target image model based on the reference imagemodel. For example, the image model building apparatus trains theadditional layer of the target image model based on an output that iscomputed from a training input based on the reference image model and anoutput that is computed from a training input based on the target imagemodel. An training input in an image format is referred to as a traininginput image. In an example, the image model building apparatus maintainsparameters of the remaining layers of the target image model whiletraining the additional layer.

FIG. 4 illustrates another example of an image model building method.The operations in FIG. 4 may be performed in the sequence and manner asshown, although the order of some operations may be changed or some ofthe operations omitted without departing from the spirit and scope ofthe illustrative examples described. Many of the operations shown inFIG. 4 may be performed in parallel or concurrently. One or more blocksof FIG. 4, and combinations of the blocks, can be implemented by specialpurpose hardware-based computer that perform the specified functions, orcombinations of special purpose hardware and computer instructions. Inaddition to the description of FIG. 4 below, the descriptions of FIGS.1-3 are also applicable to FIG. 4, and are incorporated herein byreference. Thus, the above description may not be repeated here.

Referring to FIG. 4, in operation 410, an image model building apparatusgenerates and initializes a target image model. To generate the targetimage model, the image model building apparatus connects an additionallayer to one layer (for example, a layer on an input side) from amonglayers (for example, remaining layers) that are duplicated from areference image model. In an example, the image model building apparatusinitializes the additional layer by assigning a random value to each ofnodes of the additional layer in the target image model. In an example,the image model building apparatus assigns the same value as that ofeach of nodes in the reference image model to each of nodes of thelayers duplicated from the reference image model.

In an example, a layer on an input side is a layer connected adjacent toan input layer among hidden layers of an arbitrary image model. Forexample, the layer on the input side is the layer L1 in the target imagemodel 220 of FIG. 2.

In operation 420, the image model building apparatus trains a portion ofthe target image model. For example, the image model building apparatustrains the additional layer of the target image model by updating aparameter of the additional layer. In an example, the image modelbuilding apparatus maintains parameters of the remaining layers of thetarget image model while updating the parameter of the additional layer.

In operation 430, the image model building apparatus trains all portionsof the target image model. For example, when training of the additionallayer is completed, the image model building apparatus updatesparameters of all layers in the target image model.

Thus, the image model building apparatus primarily trains the additionallayer to output a result similar to that of the reference image model.The image model building apparatus secondarily trains both theadditional layer and the remaining layers to output a more preciseresult than that of the reference image model.

An order of operations performed by the image model building apparatusis not limited to those of FIGS. 3 and 4. The order of the operationsmay vary depending on a design, or a portion of the operations may beomitted or added. Also, each of operations 410 through 430 of FIG. 4will be further described below.

FIG. 5 illustrates an example of generating and initializing a targetimage model.

FIG. 5 illustrates an example of operation 410 of FIG. 4 to generate andinitialize a target image model 520 in an image model buildingapparatus.

The image model building apparatus generates an additional layer 521 andremaining layers 522. The remaining layers 522 are layers having thesame layer structure as that of a plurality of layers 512 of a referenceimage model 510. For example, the remaining layers 522 have a structure,for example, a classifier structure, together with convolutional layersL1, L2, L3 and L4 of the reference image model 510. The image modelbuilding apparatus connects the additional layer 521 to a portion of theremaining layers 522. For example, the image model building apparatusconnects the additional layer 521 to a layer on an input side among theremaining layers 522. The additional layer 521 is indicated by, forexample, L0 in FIG. 5. However, a connection of the additional layer 521is not limited thereto.

In an example, the image model building apparatus generates theadditional layer 521 configured to process data with a size greater thanor equal to a data size that may be processed by a reference imagemodel. For example, the image model building apparatus generates atarget image model to receive an image with a resolution higher than aninput resolution of the reference image model. In an example, the imagemodel building apparatus generates an additional layer that includes anumber of nodes that is greater than or equal to a number of nodesincluded in one layer (for example, a layer on an input side in thereference image model) of the reference image model and that is lessthan or equal to a number of nodes included in an input layer of thetarget image model. Thus, the image model building apparatus extractsdetailed feature information about a high-resolution image, using anadditional layer that includes a number of nodes greater than a numberof nodes included in layers of the reference image model.

The image model building apparatus initializes the generated targetimage model 520. For example, the image model building apparatus sets aparameter of the target image model 520 to an initial value. The imagemodel building apparatus determines the same parameters of the remaininglayers 522 of the target image model 520 as those of a pre-trainedreference image model 510 as initial parameters of the remaining layers522. The image model building apparatus performs a random initializationof the additional layer 521. For example, the image model buildingapparatus determines a random value for the additional layer 521 as aninitial parameter.

Thus, the target image model 520 includes the additional layer 521 thatis randomly initialized, and the remaining layers 522 that areduplicated from the reference image model 510.

FIG. 6 is a diagram illustrating an example of training a target imagemodel. The operations in FIG. 6 may be performed in the sequence andmanner as shown, although the order of some operations may be changed orsome of the operations omitted without departing from the spirit andscope of the illustrative examples described. Many of the operationsshown in FIG. 6 may be performed in parallel or concurrently. One ormore blocks of FIG. 6, and combinations of the blocks, can beimplemented by special purpose hardware-based computer that perform thespecified functions, or combinations of special purpose hardware andcomputer instructions. In addition to the description of FIG. 6 below,the descriptions of FIGS. 1-5 are also applicable to FIG. 6, and areincorporated herein by reference. Thus, the above description may not berepeated here.

FIG. 6 illustrates an example of operation 420 of FIG. 4 to train aportion of a target image model.

Referring to FIG. 6, in operation 621, an image model building apparatusacquires a high-resolution training input image. For example, the imagemodel building apparatus acquires new training data with ahigh-resolution (for example, a second resolution).

The new training data is data used to train a target image model, andincludes, for example, a training input image with the secondresolution. The new training data represents an image of a higherresolution in comparison to the original training data indicating afirst resolution. The new training data indicates the same object as anobject included in the original training data, however, examples are notlimited thereto. The new training data is data acquired regardless ofthe original training data. For example, the new training data furtherincludes a label mapped to the training input image with the secondresolution, however, examples are not limited thereto.

In operation 622, the image model building apparatus calculates anoutput of the target image model. For example, the image model buildingapparatus calculates a target model output from a training input withthe second resolution based on the target image model. In an example,when the target image model is implemented as a neural network, theimage model building apparatus computes the output of the target imagemodel by forward propagating a training input from an input layer to anoutput layer of the target image model.

In operation 623, the image model building apparatus converts thetraining input image based on an input layer of a reference image model.The image model building apparatus generates a conversion image byconverting a training input based on the input layer of the referenceimage model. For example, the image model building apparatus identifiesa resolution that may be input to the reference image model, based onthe input layer of the reference image model, and converts the traininginput image into an image with the identified resolution. The imagemodel building apparatus converts the training input image with thesecond resolution into an image with the first resolution supported bythe reference image model.

The image model building apparatus converts an image format of thetraining input image into an image format corresponding to the firstresolution. For example, the image model building apparatus resizes thetraining input image or down-samples pixels included in the traininginput image, to convert the image format of the training input image.The conversion image is an image that has an image format into which theimage format of the training input image is converted.

In operation 624, the image model building apparatus calculates anoutput of the reference image model. The image model building apparatuscomputes a reference model output from an input (for example, theconversion image) into which the training input is converted, based onthe reference image model. For example, the image model buildingapparatus computes the reference model output by forward propagating theconversion image from the input layer and an output layer of thereference image model.

In operation 625, the image model building apparatus computes an errorbased on the output of the target image model and the output of thereference image model. The image model building apparatus trains thetarget image model based on the reference model output. The image modelbuilding apparatus computes an error based on the reference model outputand the target model output. For example, the image model buildingapparatus calculates a loss function to train the target image model,based on at least one of the reference model output and the target modeloutput. The loss function is a function indicating a difference betweenthe reference model output and the target model output, such as, forexample, a softmax loss function, or a cross-entropy loss function,however, the loss function is not limited thereto. For example, whentraining data includes a pair of a training input image and a traininglabel, the loss function may be a function indicating a relationshipbetween the target model output and the training label.

In operation 626, the image model building apparatus updates a parameterof an additional layer of the target image model based on the computederror. The image model building apparatus trains the target image modelbased on the reference model output and the target model output that arebased on the reference image model and the target image model,respectively.

The image model building apparatus updates a parameter of at least aportion of layers of the target image model based on the computed error.For example, the image model building apparatus updates the parameter ofthe additional layer of the target image model, using a gradient-basedback-propagation scheme. In this example, the image model buildingapparatus maintains parameters of remaining layers of the target imagemodel while updating the parameter of the additional layer.

In operation 627, the image model building apparatus determines whetherthe error converges. The image model building apparatus repeats updatingof the parameter until an error between the reference model output andthe target model output converges. In an example, when the parameter ofthe additional layer is updated in the target image model, and when achange in the error between the reference model output and the targetmodel output is less than or equal to a threshold, the image modelbuilding apparatus determines that the error converges. In anotherexample, when a difference between an updated parameter and a parameterthat is not updated is less than or equal to a threshold, the imagemodel building apparatus determines that the error converges. When it isdetermined that the error does not converge, the image model buildingapparatus reverts to operation 621 and updates a parameter based on anew training input.

Thus, the image model building apparatus minimizes an error that isbased on the reference image model and the target image model byrepeating updating of the parameter of the additional layer.

The image model building apparatus minimizes a time and computationalrecourses used to train the target image model by restricting a targetwith a parameter to be updated to the additional layer. The image modelbuilding apparatus trains the target image model to have a recognitionperformance similar to that of the reference image model by minimizingan error between the reference model output and the target model output.Because a target for training is restricted to the additional layer, theimage model building apparatus trains the target image model whilerobustly maintaining a recognition performance for a low-resolutionimage acquired from the reference image model.

FIG. 7 illustrates an example of computing an error during training.

The error is computed based on the output of the target image model andthe output of the reference image model in operations 625 and 626 ofFIG. 6, however, examples are not limited thereto. For example, theimage model building apparatus trains an additional layer 721 based onfeature data output from at least a portion of layers of a referenceimage model 710 and feature data output from at least a portion oflayers of a target image model 720. A conversion image input to thereference image model 710 is generated by converting a training input inoperation 701.

Referring to FIG. 7, in operation 709, the image model buildingapparatus computes an error based on feature data of the reference imagemodel 710 and feature data of the target image model 720. For example,the image model building apparatus computes the error using anauto-encoding scheme. The image model building apparatus computes theerror based on a difference between feature data output from each of thelayers of the reference image model 710 and feature data output from alayer of the target image model 720 corresponding to each of the layersof the reference image model 710. For example, the image model buildingapparatus computes an error based on a difference between features oflayers L1, a difference between features of layers L2, a differencebetween features of layers L3, and a difference between features oflayers L4. The layers L1, L2, L3 and L4 are included in the referenceimage model 710 and the target image model 720.

The image model building apparatus updates a parameter of the additionallayer 721 based on a difference between features of a plurality oflayers 712 of the reference image model 710 and features of remaininglayers 722 of the target image model 720.

FIG. 8 illustrates an example of training all portions of a target imagemodel. The operations in FIG. 8 may be performed in the sequence andmanner as shown, although the order of some operations may be changed orsome of the operations omitted without departing from the spirit andscope of the illustrative examples described. Many of the operationsshown in FIG. 8 may be performed in parallel or concurrently. One ormore blocks of FIG. 8, and combinations of the blocks, can beimplemented by special purpose hardware-based computer that perform thespecified functions, or combinations of special purpose hardware andcomputer instructions. In addition to the description of FIG. 8 below,the descriptions of FIGS. 1-7 are also applicable to FIG. 8, and areincorporated herein by reference. Thus, the above description may not berepeated here.

FIG. 8 illustrates an example of operation 430 of FIG. 4 to train allportions of the target image model. When training of an additional layeris completed, an image model building apparatus updates parameters ofall layers in the target image model.

Referring to FIG. 8, in operation 831, the image model buildingapparatus acquires a high-resolution input image. For example, the imagemodel building apparatus acquires a new input image. The new input imagecorresponds to the training input image acquired in operation 621 ofFIG. 6, however, examples are not limited thereto. The new input imageis, for example, an image that is additionally acquired independently ofthe training input image. For example, when the image model buildingapparatus is included in an autonomous vehicle, the new input image isan image acquired when the autonomous vehicle is moving.

In operation 832, the image model building apparatus determines a labelof the high-resolution input image. The image model building apparatusdetermines a label of a new input image with a second resolution.

In an example, when the target image model is a model for aclassification task, the image model building apparatus generates groundtruth label information for the new input image with the secondresolution. In another example, when the target image model is a modelto detect a size and a location of an object in an image, the imagemodel building apparatus generates label information that indicates asize and a location of an object in the new input image. Also, the imagemodel building apparatus uses label information that is manually inputby an expert.

Also, the image model building apparatus determines a label of the newinput image based on a user input associated with an acquisition of thenew input image. For example, when the image model building apparatus ismounted in an autonomous vehicle, and when a driver finds an obstacle,the driver operates a brake pad. The image model building apparatusgenerates label information that is associated with a new input imageacquired at a point in time at which a user input, such as a useroperation of the brake pad, is acquired, and that indicates that adetected object is true. However, this is merely an example, and anautomated generation of label information based on a user input may varydepending on a design.

However, when the target image model is designed for an unsupervisedimage domain translation task, operation 832 is not performed.

In operation 833, the image model building apparatus updates theparameters of the target image model. For example, the image modelbuilding apparatus additionally trains the target image model based onthe new input image and the label. When the training of the additionallayer is completed, the image model building apparatus updates all theparameters of the target image model, to fine tune the parameters of thetarget image model. The image model building apparatus computes anoutput based on the training image model from a training input imagewith a second resolution acquired in operations 831 and 832. The imagemodel building apparatus computes an error based on the computed outputand label information, and updates parameters of all the layers in thetarget image model based on a back-propagation scheme using the computederror.

In operation 834, the image model building apparatus determines whetherthe error converges. The image model building apparatus repeats trainingof all portions of the target image model until the error converges. Inan example, when the error converges, the image model building apparatusterminates the training of the target image model. In another example,when the error does not converge, the image model building apparatusreverts to operation 831 and repeats the training of all the portions oftarget image model.

Thus, the image model building apparatus efficiently and robustly trainsthe target image model based on a lower quantity of training data usinga pre-trained reference image model. Also, the image model buildingapparatus minimizes computational and temporal costs used to train thetarget image model.

FIG. 9 illustrates an example of growing a target image model.

An image model building apparatus grows an image model that is generatedby the operations described above with reference to FIGS. 1 through 8.For example, when training of a target image model 920 is completed, theimage model building apparatus generates an additional image model 930having an additional layer L0′ connected to the target image model 920.

The image model building apparatus generates the target image model 920by connecting an additional layer L0 to a plurality of layers (forexample, layers L1 through L4) of the reference image model 910. Theimage model building apparatus generates the additional image model 930by connecting the additional layer L0′ to a plurality of layers (forexample, the additional layer L0 and layers L1 through L4) of the targetimage model 920.

The image model building apparatus trains the additional layer L0′ ofthe additional image model 930 based on an output of the target imagemodel 920. The additional image model 930 is trained similarly totraining of the target image model 920. For example, the image modelbuilding apparatus acquires a training input image with a thirdresolution, inputs the training input image to the additional imagemodel 930, and computes a third temporary output. The image modelbuilding apparatus converts the training input image with the thirdresolution into an image with a second resolution, inputs the image withthe second resolution to the target image model 920, and computes asecond temporary output. The image model building apparatus converts theimage with the second resolution into an image with a first resolution,inputs the image with the first resolution to the reference image model910, and computes a first temporary output. The image model buildingapparatus computes an error based on any one or any combination of thefirst temporary output, the second temporary output and the thirdtemporary output in operation 909. The image model building apparatusupdates a parameter of the additional layer L0′ of the additional imagemodel 930 based on the computed error. The second resolution is higherthan the first resolution, and the third resolution is higher than thesecond resolution.

The image model building apparatus grows an image model as describedabove, and thus it is possible to quickly and efficiently generate andtrain an image model for a resolution supported by a camera sensor eventhough a performance of the camera sensor changes.

A trained target image model that is installed in an image recognitionapparatus. The image recognition apparatus acquires an input image, andgenerates a recognition result based on the target image model from theacquired input image. For example, the image recognition apparatusextracts feature data from the input image based on the target imagemodel, or determines a label indicted by the input image. The imagerecognition apparatus recognizes an object appearing in the input imagebased on the target image model. When the image recognition apparatus isinstalled in a vehicle, the image recognition apparatus recognizes anobstacle appearing in the input image.

FIGS. 10 and 11 are diagrams illustrating examples of a configuration ofan image model building apparatus.

FIG. 10 illustrates an example of a configuration of an image modelbuilding apparatus 1000. The image model building apparatus 1000includes a processor 1010 and a memory 1020.

The processor 1010 generates a target image model that includes the samelayers as layers of a reference image model, and an additional layer.Also, the processor 1010 trains the additional layer of the target imagemodel based on the reference image model. However, operations of theprocessor 1010 are not limited thereto, and the processor 1010 performsat least a portion of the operations described above with reference toFIGS. 1 through 9. Further details of the processor 1010 are providedbelow.

The memory 1020 stores the trained target image model. Also, the memory1020 stores the reference image model and an additional image model.Further details of the memory 1020 are provided below.

FIG. 11 illustrates an example of a configuration of an image modelbuilding apparatus 1100 configured to perform active leaning. The imagemodel building apparatus 1100 includes a processor 1110, a memory 1120,and an image acquirer 1130.

The processor 1110 performs operations similar to that of the processor1010 of FIG. 10. When a new input image is acquired by the imageacquirer 1130, the processor 1110 generates and trains a target imagemodel 1122 based on the new input image. For example, the processor 1110generates label information associated with the new input image acquiredby the image acquirer 1130, and trains the target image model 1122 basedon the new input image and the generated label information. However, theoperations of the processor 1110 are not limited thereto, and theprocessor 1110 performs an operation connected to at least a portion ofthe operations described above with reference to FIGS. 1 through 9.

The memory 1120 temporarily or permanently stores data used to train thetarget image model 1122. For example, the memory 1120 stores a referenceimage model 1121, the target image model 1122 and training data 1123. Inan example, when training of the target image model 1122 is completed,the memory 1120 deletes the reference image model 1121. The trainingdata 1123 includes a training input image that is acquired in advance.The training data 1123 also includes a new input image that is acquiredby the image acquirer 1130.

The image acquirer 1130 acquires an image used to train the target imagemodel 1122. For example, the image acquirer 1130 includes a camerasensor, and the camera sensor is configured to capture an externalappearance of a device (for example, an autonomous vehicle). The imageacquirer 1130 acquires an external image in response to a request fromthe processor 1110 (for example, in response to a user input beingreceived). However, examples are not limited thereto, and the imageacquirer 1130 periodically or continuously acquire an image. Also, theimage acquirer 1130 acquires an input image for training through acommunication (for example, a wireless communication or a wiredcommunication) with an external device (for example, an arbitraryserver).

In addition to operations of one or more of the neural networkprocessing apparatuses and/or operations described in FIGS. 1-11 asnoted above, the memory 1120 may further store instructions which, whenexecuted by processor 1110, cause the processor 1110 to performadditional operations, functions, and controls of the image modelbuilding apparatus 1100, such as a user interface of the image modelbuilding apparatus 1100.

The image model building apparatus 1100 may be connected to an externaldevice, for example, a personal computer (PC) or a network, via aninput/output device of the electronic system, to exchange data with theexternal device. The image model building apparatus 1100 may beimplemented in various electronic devices, as only non-limitingexamples, a mobile device, for example, a mobile telephone, asmartphone, a wearable smart device (such as, a ring, a watch, a pair ofglasses, glasses-type device, a bracelet, an ankle bracket, a belt, anecklace, an earring, a headband, a helmet, a device embedded in thecloths, or an eye glass display (EGD)), a computing device, for example,a server, a laptop, a notebook, a subnotebook, a netbook, anultra-mobile PC (UMPC), a tablet personal computer (tablet), a phablet,a mobile internet device (MID), a personal digital assistant (PDA), anenterprise digital assistant (EDA), an ultra mobile personal computer(UMPC), a portable lab-top PC, electronic product, for example, a robot,a digital camera, a digital video camera, a portable game console, anMP3 player, a portable/personal multimedia player (PMP), a handhelde-book, a global positioning system (GPS) navigation, a personalnavigation device, portable navigation device (PND), a handheld gameconsole, an e-book, a television (TV), a high definition television(HDTV), a smart TV, a smart appliance, a smart home device, or asecurity device for gate control, various Internet of Things (IoT)devices, or any other device capable of wireless communication ornetwork communication consistent with that disclosed herein.

The image model building apparatuses 1000 and 1100, and otherapparatuses, units, modules, devices, and other components describedherein with respect to FIGS. 10 and 11 are implemented by hardwarecomponents. Examples of hardware components that may be used to performthe operations described in this application where appropriate includecontrollers, sensors, generators, drivers, memories, comparators,arithmetic logic units, adders, subtractors, multipliers, dividers,integrators, and any other electronic components configured to performthe operations described in this application. In other examples, one ormore of the hardware components that perform the operations described inthis application are implemented by computing hardware, for example, byone or more processors or computers. A processor or computer may beimplemented by one or more processing elements, such as an array oflogic gates, a controller and an arithmetic logic unit, a digital signalprocessor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 3, 4, 6 and 8 that perform theoperations described in this application are performed by computinghardware, for example, by one or more processors or computers,implemented as described above executing instructions or software toperform the operations described in this application that are performedby the methods. For example, a single operation or two or moreoperations may be performed by a single processor, or two or moreprocessors, or a processor and a controller. One or more operations maybe performed by one or more processors, or a processor and a controller,and one or more other operations may be performed by one or more otherprocessors, or another processor and another controller. One or moreprocessors, or a processor and a controller, may perform a singleoperation, or two or more operations.

Instructions or software to control a processor or computer to implementthe hardware components and perform the methods as described above arewritten as computer programs, code segments, instructions or anycombination thereof, for individually or collectively instructing orconfiguring the processor or computer to operate as a machine orspecial-purpose computer to perform the operations performed by thehardware components and the methods as described above. In an example,the instructions or software includes at least one of an applet, adynamic link library (DLL), middleware, firmware, a device driver, anapplication program storing the method of building an image model. Inone example, the instructions or software include machine code that isdirectly executed by the processor or computer, such as machine codeproduced by a compiler. In another example, the instructions or softwareinclude higher-level code that is executed by the processor or computerusing an interpreter. Programmers of ordinary skill in the art canreadily write the instructions or software based on the block diagramsand the flow charts illustrated in the drawings and the correspondingdescriptions in the specification, which disclose algorithms forperforming the operations performed by the hardware components and themethods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access programmable readonly memory (PROM), electrically erasable programmable read-only memory(EEPROM), random-access memory (RAM), dynamic random access memory(DRAM), static random access memory (SRAM), flash memory, non-volatilememory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-rayor optical disk storage, hard disk drive (HDD), solid state drive (SSD),flash memory, a card type memory, such as, a multimedia card, a securedigital (SD) card or a extreme digital (XD) card, magnetic tapes, floppydisks, magneto-optical data storage devices, optical data storagedevices, hard disks, solid-state disks, and any other device that isconfigured to store the instructions or software and any associateddata, data files, and data structures in a non-transitory manner andproviding the instructions or software and any associated data, datafiles, and data structures to a processor or computer so that theprocessor or computer can execute the instructions. Examples of anon-transitory computer-readable storage medium include read-only memory(ROM), random-access memory (RAM), CD-ROMs, CD-Rs, CD+Rs, CD-RWs,CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs,BD-Rs, BD-R LTHs, BD-REs, magnetic tapes, floppy disks, magneto-opticaldata storage devices, optical data storage devices, hard disks,solid-state disks, and any other device that is configured to store theinstructions or software and any associated data, data files, and datastructures in a non-transitory manner and provide the instructions orsoftware and any associated data, data files, and data structures to oneor more processors or computers so that the one or more processors orcomputers can execute the instructions. In one example, the instructionsor software and any associated data, data files, and data structures aredistributed over network-coupled computer systems so that theinstructions and software and any associated data, data files, and datastructures are stored, accessed, and executed in a distributed fashionby the one or more processors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A method of building an image model, the methodcomprising: generating a target image model comprising an additionallayer connected to a layer located on an input side of remaining layersin the target image model, wherein the remaining layers are same aslayers of a reference image model, and the target image model and thereference image model include a neural network; performing a firsttraining for the additional layer of the target image model based on adifference between feature data output from at least a portion of layersof the reference image model and feature data output from at least aportion of layers of the target image model; and in response to acompletion of the first training of the additional layer, performing asecond training for all layers of the target image model based on thereference image model, the all layers of the target image modelincluding the additional layer and the remaining layers.
 2. The methodof claim 1, wherein the generating of the target image model comprisesgenerating the target image model by connecting the additional layer toa layer located on an input side of the remaining layers in the targetimage model.
 3. The method of claim 1, wherein the generating of thetarget image model comprises initializing the additional layer byassigning a random value to each nodes of the additional layer.
 4. Themethod of claim 1, wherein the training of the additional layercomprises: determining a reference model output from an image with aconverted training input, based on the reference image model; andtraining the target image model based on the reference model output. 5.The method of claim 4, wherein the determining of the reference modeloutput comprises: generating a conversion image by converting thetraining input based on an input layer of the reference image model; andcomputing the reference model output from the conversion image.
 6. Themethod of claim 1, wherein the training of the additional layercomprises training the target image model based on a reference modeloutput and a target model output that are based on the reference imagemodel and the target image model, respectively.
 7. The method of claim6, wherein the training of the additional layer comprises: computing anerror based on the reference model output and the target model output;and updating a parameter of at least a portion of the remaining layersand the additional layer in the target image model based on the computederror.
 8. The method of claim 7, wherein the updating of the parametercomprises repeating updating of the parameter until an error between thereference model output and the target model output converges.
 9. Themethod of claim 1, wherein the training of the additional layercomprises updating a parameter of the additional layer.
 10. The methodof claim 1, wherein the training of the additional layer comprisesupdating a parameter of the additional layer while maintainingparameters of the remaining layers in the target image model.
 11. Themethod of claim 1, wherein the generating of the target image modelcomprises: generating the remaining layers of the target image model byduplicating parameters and a layer structure of the reference imagemodel; and connecting the additional layer to the remaining layers. 12.The method of claim 1, further comprising: updating parameters of theremaining layers and the additional layer in the target image model, inresponse to the completion of the first training of the additionallayer.
 13. The method of claim 1, wherein a number of nodes in theadditional layer is greater than or equal to a number of nodes in one ofthe layers of the reference image model and is less than or equal to anumber of nodes in an input layer of the target image model.
 14. Themethod of claim 1, further comprising: acquiring an input image;determining a label of the input image; and additionally training thetarget image model based on the input image and the label.
 15. Themethod of claim 14, wherein the determining of the label comprisesdetermining the label based on a user input associated with theacquiring of the input image.
 16. The method of claim 1, furthercomprising: generating an additional image model comprising anadditional layer connected to the target image model, in response to acompletion of the first training of the target image model; and trainingthe additional layer of the additional image model based on an output ofthe target image model.
 17. The method of claim 1, wherein the targetimage model is configured to receive an image with a resolution higherthan an input resolution of the reference image model.
 18. The method ofclaim 1, wherein the training of the additional layer comprises trainingthe additional layer based on feature data output from at least aportion of the layers of the reference image model and feature dataoutput from at least a portion of the remaining layers of the targetimage model.
 19. The method of claim 5, wherein the resolution of theimage is greater than a resolution of the conversion image.
 20. Themethod of claim 1, wherein a number of nodes in the additional layer isgreater than a number of nodes in any of the layers of the referenceimage model.
 21. A non-transitory computer-readable storage mediumstoring instructions that, when executed by a processor, cause theprocessor to perform the method of claim
 1. 22. An apparatus forbuilding an image model, the apparatus comprising: a processorconfigured to: generate a target image model that comprises anadditional layer connected to a layer located on an input side ofremaining layers in the target image model, wherein the remaining layersare same as layers of a reference image model, and the target imagemodel and the reference image model include a neural network; perform afirst training for the additional layer of the target image model basedon a difference between feature data output from at least a portion oflayers of the reference image model and feature data output from atleast a portion of layers of the target image model; and in response toa completion of the first training of the additional layer, perform asecond training for all layers of the target image model based on thereference image model, the all layers of the target image modelincluding the additional layer and the remaining layers; and a memoryconfigured to store the trained target image model.